class_docstr: Classifier head for Vision Transformer
  
  Args:
  ----
      in_channels: number of input channels
      num_classes: number of output classes
class_docstr_tok: ["Classifier","head","for","Vision","Transformer","Args",":","--","--","in_channels",":","number","of","input","channels","num_classes",":","number","of","output","classes"]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      # (batch_size, num_classes) cls token
      return self.head(x[:, 0])
  
file_path: doctr/doctr/models/classification/vit/pytorch.py
imports: from copy import deepcopy
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  from torch import nn
  from doctr.datasets import VOCABS
  from doctr.models.modules.transformer import EncoderBlock
  from doctr.models.modules.vision_transformer.pytorch import PatchEmbedding
  from ...utils.pytorch import load_pretrained_params
label_desc: Use image node_id 2 for calling the ClassifierHead obj's underlying member method code with example usage: obj.forward(x) and returns: self